OpenRaiser/Intern-Atlas huggingface dataset:
Towards the Long-Endurance Flight of an Insect-Inspired, Tailless, Two-Winged, Flapping-Wing Flying Robot
Venue: arXiv cs.RO 2020
Year: 2020

A hover-capable insect-inspired flying robot that can remain long in the air has shown its potential use for both confined indoor and outdoor applications to complete assigned tasks. In this letter, we report improvements in the flight endurance of our 15.8 g robot, named KUBeetle-S, using a low-voltage power source. The robot is equipped with a simple but effective control mechanism that can modulate the stroke plane for attitude stabilization and control. Due to the demand for extended flight, we performed a series of experiments on the lift generation and power requirement of the robot with different stroke amplitudes and wing areas. We show that a larger wing with less inboard wing area improves the lift-to-power ratio and produces a peak lift-to-weight ratio of 1.34 at 3.7 V application. Flight tests show that the robot employing the selected wing could hover for 8.8 minutes. Moreover, the robot could perform maneuvers in any direction, fly outdoors, and carry payload, demonstrating its ability to enter the next phase of autonomous flight.
Prive-HD: Privacy-Preserved Hyperdimensional Computing
Venue: arXiv cs.LG 2020
Year: 2020

The privacy of data is a major challenge in machine learning as a trained model may expose sensitive information of the enclosed dataset. Besides, the limited computation capability and capacity of edge devices have made cloud-hosted inference inevitable. Sending private information to remote servers makes the privacy of inference also vulnerable because of susceptible communication channels or even untrustworthy hosts. In this paper, we target privacy-preserving training and inference of brain-inspired Hyperdimensional (HD) computing, a new learning algorithm that is gaining traction due to its light-weight computation and robustness particularly appealing for edge devices with tight constraints. Indeed, despite its promising attributes, HD computing has virtually no privacy due to its reversible computation. We present an accuracy-privacy trade-off method through meticulous quantization and pruning of hypervectors, the building blocks of HD, to realize a differentially private model as well as to obfuscate the information sent for cloud-hosted inference. Finally, we show how the proposed techniques can be also leveraged for efficient hardware implementation.
Dense-Resolution Network for Point Cloud Classification and Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Point cloud analysis is attracting attention from Artificial Intelligence research since it can be widely used in applications such as robotics, Augmented Reality, self-driving. However, it is always challenging due to irregularities, unorderedness, and sparsity. In this article, we propose a novel network named Dense-Resolution Network (DRNet) for point cloud analysis. Our DRNet is designed to learn local point features from the point cloud in different resolutions. In order to learn local point groups more effectively, we present a novel grouping method for local neighborhood searching and an error-minimizing module for capturing local features. In addition to validating the network on widely used point cloud segmentation and classification benchmarks, we also test and visualize the performance of the components. Comparing with other state-of-the-art methods, our network shows superiority on ModelNet40, ShapeNet synthetic and ScanObjectNN real point cloud datasets.
Lattice-based Unidirectional IBPRE Secure in Standard Model
Venue: arXiv cs.CR 2020
Year: 2020

Proxy re-encryption (PRE) securely enables the re-encryption of ciphertexts from one key to another, without relying on trusted parties, i.e., it offers delegation of decryption rights. PRE allows a semi-trusted third party termed as a "proxy" to securely divert encrypted files of user A (delegator) to user B (delegatee) without revealing any information about the underlying files to the proxy. To eliminate the necessity of having a costly certificate verification process, Green and Ateniese introduced an identity-based PRE (IB-PRE). The potential applicability of IB-PRE leads to intensive research from its first instantiation. Unfortunately, till today, there is no unidirectional IB-PRE secure in the standard model, which can withstand quantum attack. In this paper, we provide, for the first time, a concrete construction of unidirectional IB-PRE which is secure in standard model based on the hardness of learning with error problem. Our technique is to use the novel trapdoor delegation technique of Micciancio and Peikert. The way we use trapdoor delegation technique may prove useful for functionalities other than proxy re-encryption as well.
Detection of Intermittent Faults Based on an Optimally Weighted Moving Average T^2 Control Chart with Stationary Observations
Venue: arXiv cs.IT 2020
Year: 2020

The moving average (MA)-type scheme, also known as the smoothing method, has been well established within the multivariate statistical process monitoring (MSPM) framework since the 1990s. However, its theoretical basis is still limited to smoothing independent data, and the optimality of its equally or exponentially weighted scheme remains unproven. This paper aims to weaken the independence assumption in the existing MA method, and then extend it to a broader area of dealing with autocorrelated weakly stationary processes. With the discovery of the non-optimality of the equally and exponentially weighted schemes used for fault detection when data have autocorrelation, the essence that they do not effectively utilize the correlation information of samples is revealed, giving birth to an optimally weighted moving average (OWMA) theory. The OWMA method is combined with the Hotelling's $T^2$ statistic to form an OWMA $T^2$ control chart (OWMA-TCC), in order to detect a more challenging type of fault, i.e., intermittent fault (IF). Different from the MA scheme that puts an equal weight on samples within a time window, OWMA-TCC uses correlation (autocorrelation and cross-correlation) information to find an optimal weight vector (OWV) for the purpose of IF detection (IFD). In order to achieve a best IFD performance, the concept of IF detectability is defined and corresponding detectability conditions are provided, which further serve as selection criteria of the OWV. Then, the OWV is given in the form of a solution to nonlinear equations, whose existence is proven with the aid of the Brouwer fixed-point theory. Moreover, symmetrical structure of the OWV is revealed, and the optimality of the MA scheme for any IF directions when data exhibit no autocorrelation is proven.
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning
Venue: arXiv cs.LG 2020
Year: 2020

Machine learning is being integrated into a growing number of critical systems with far-reaching impacts on society. Unexpected behaviour and unfair decision processes are coming under increasing scrutiny due to this widespread use and its theoretical considerations. Individuals, as well as organisations, notice, test, and criticize unfair results to hold model designers and deployers accountable. We offer a framework that assists these groups in mitigating unfair representations stemming from the training datasets. Our framework relies on two inter-operating adversaries to improve fairness. First, a model is trained with the goal of preventing the guessing of protected attributes' values while limiting utility losses. This first step optimizes the model's parameters for fairness. Second, the framework leverages evasion attacks from adversarial machine learning to generate new examples that will be misclassified. These new examples are then used to retrain and improve the model in the first step. These two steps are iteratively applied until a significant improvement in fairness is obtained. We evaluated our framework on well-studied datasets in the fairness literature -- including COMPAS -- where it can surpass other approaches concerning demographic parity, equality of opportunity and also the model's utility. We also illustrate our findings on the subtle difficulties when mitigating unfairness and highlight how our framework can assist model designers.
Developing Accessible Mobile Applications with Cross-Platform Development Frameworks
Venue: arXiv cs.SE 2020
Year: 2020

This contribution investigates how cross-platform development frameworks (CPDF) support the creation of mobile applications that are accessible to people with visual impairments through screen readers. We first systematically analyze screen-reader APIs available in native iOS and Android, and we examine whether and at what level the same functionalities are available in two popular CPDF: Xamarin and React Native. This analysis unveils that there are many functionalities shared between native iOS and Android APIs, but most of them are not available in React Native or Xamarin. In particular, not even all basic APIs are exposed by the examined CPDF. Accessing the unavailable APIs is still possible, but it requires an additional effort by the developers who need to know native APIs and to write platform specific code, hence partially negating the advantages of CPDF. To address this problem, we consider a representative set of native APIs that cannot be directly accessed from React Native and Xamarin and show sample implementations for accessing them.
Neural Networks Versus Conventional Filters for Inertial-Sensor-based Attitude Estimation
Venue: arXiv cs.LG 2020
Year: 2020

Inertial measurement units are commonly used to estimate the attitude of moving objects. Numerous nonlinear filter approaches have been proposed for solving the inherent sensor fusion problem. However, when a large range of different dynamic and static rotational and translational motions is considered, the attainable accuracy is limited by the need for situation-dependent adjustment of accelerometer and gyroscope fusion weights. We investigate to what extent these limitations can be overcome by means of artificial neural networks and how much domain-specific optimization of the neural network model is required to outperform the conventional filter solution. A diverse set of motion recordings with a marker-based optical ground truth is used for performance evaluation and comparison. The proposed neural networks are found to outperform the conventional filter across all motions only if domain-specific optimizations are introduced. We conclude that they are a promising tool for inertial-sensor-based real-time attitude estimation, but both expert knowledge and rich datasets are required to achieve top performance.
Cognitive Amplifier for Internet of Things
Venue: arXiv cs.AI 2020
Year: 2020

We present a Cognitive Amplifier framework to augment things part of an IoT, with cognitive capabilities for the purpose of improving life convenience. Specifically, the Cognitive Amplifier consists of knowledge discovery and prediction components. The knowledge discovery component focuses on finding natural activity patterns considering their regularity, variations, and transitions in real life setting. The prediction component takes the discovered knowledge as the base for inferring what, when, and where the next activity will happen. Experimental results on real-life data validate the feasibility and applicability of the proposed approach.
Towards NLP-supported Semantic Data Management
Venue: arXiv cs.IR 2020
Year: 2020

The heterogeneity of data poses a great challenge when data from different sources is to be merged for one application. Solutions for this are offered, for example, by ontology-based data management (OBDM). A challenge of OBDM is the automatic creation of semantic models from datasets. This process is typically performed either data- or label-driven and always involves manual human intervention. We identified textual descriptions of data, a form of metadata, quickly to be produced and consumed by humans, as third possible basis for automatic semantic modelling. In this paper, we present, how we plan to use textual descriptions to enhance semantic data management. We will use state of the art NLP technologies to identify concepts within textual descriptions and build semantic models from this in combination with an evolving ontology. We will use automatically identified models in combination with the human data provider to automatically extend the ontology so that it learns new verified concepts over time. Finally, we will use the created ontology and automatically identified semantic models to either rate descriptions for new data sources or even to automatically generate descriptive texts that are easier to understand by the human user than formal models. We present the procedure which we plan for the ongoing research, as well as expected outcomes.
Providing a way to create balance between reliability and delays in SDN networks by using the appropriate placement of controllers
Venue: arXiv cs.NI 2020
Year: 2020

Computer networks covered the entire world and a serious and new development has not formed for many years. But companies and consumer organizations complain about the failure to add new features to their networks and according to their need, like much of the works to be done automatically and they also like to develop and expand their networks on the software side so they do not need new expensive hardware for many of the activities and needs of their network. Analysis of the control layers and writing data in Software-Defined Network facilitate network management and accelerate innovation in network. In order to develop broad networks of SDN, often a large number controller is needed and located position of controllers in the SDN networks and can be raised as an important and basic issue in the field of SDN network and have impact on reliability of SDN networks. This paper focused on latency and reliability in SDN networks. The latency here means the delay in response to the request of data path that has a significant impact on network latency. In this paper it is shown that the number of controllers and their position can be effective on two measures; reliability and latency in SDN networks.
Demo: A Unified Platform of Free-Space Optics for High-Quality Video Transmission
Venue: arXiv cs.NI 2020
Year: 2020

In this paper, we investigate video signal transmission through an FPGA-based free-space optical (FSO) communication system prototype. We use a channel emulator that models the turbulence, scintillation, and power attenuation of the FSO channel and the FPGA-based real-time prototype for processing transmitted and received video signals. We vary the setup environment of the channel emulator by changing the amount of turbulence and wind speed. At the end of the demonstration, we show that through our testbed, even 4K ultra-high-definition (UHD) resolution video with 60 fps can be successfully transmitted under high turbulence and wind speed.
Communication-Efficient Gradient Coding for Straggler Mitigation in Distributed Learning
Venue: arXiv cs.IT 2020
Year: 2020

Distributed implementations of gradient-based methods, wherein a server distributes gradient computations across worker machines, need to overcome two limitations: delays caused by slow running machines called 'stragglers', and communication overheads. Recently, Ye and Abbe [ICML 2018] proposed a coding-theoretic paradigm to characterize a fundamental trade-off between computation load per worker, communication overhead per worker, and straggler tolerance. However, their proposed coding schemes suffer from heavy decoding complexity and poor numerical stability. In this paper, we develop a communication-efficient gradient coding framework to overcome these drawbacks. Our proposed framework enables using any linear code to design the encoding and decoding functions. When a particular code is used in this framework, its block-length determines the computation load, dimension determines the communication overhead, and minimum distance determines the straggler tolerance. The flexibility of choosing a code allows us to gracefully trade-off the straggler threshold and communication overhead for smaller decoding complexity and higher numerical stability. Further, we show that using a maximum distance separable (MDS) code generated by a random Gaussian matrix in our framework yields a gradient code that is optimal with respect to the trade-off and, in addition, satisfies stronger guarantees on numerical stability as compared to the previously proposed schemes. Finally, we evaluate our proposed framework on Amazon EC2 and demonstrate that it reduces the average iteration time by 16% as compared to prior gradient coding schemes.
Applying a Formal Method in Industry: a 25-Year Trajectory
Venue: arXiv cs.SE 2020
Year: 2020

Industrial applications involving formal methods are still exceptions to the general rule. Lack of understanding, employees without proper education, difficulty to integrate existing development cycles, no explicit requirement from the market, etc. are explanations often heard for not being more formal. Hence the feedback provided by industry to academics is not as constructive as it might be. Summarizing a 25-year return of experience in the effective application of a formal method - namely B and Event-B - in diverse application domains (railways, smartcard, automotive), this article makes clear why and where formal methods have been applied, explains the added value obtained so far, and tries to anticipate the future of these two formalisms for safety critical systems.
VirAAL: Virtual Adversarial Active Learning For NLU
Venue: arXiv cs.CL 2020
Year: 2020

This paper presents VirAAL, an Active Learning framework based on Adversarial Training. VirAAL aims to reduce the effort of annotation in Natural Language Understanding (NLU). VirAAL is based on Virtual Adversarial Training (VAT), a semi-supervised approach that regularizes the model through Local Distributional Smoothness. With that, adversarial perturbations are added to the inputs making the posterior distribution more consistent. Therefore, entropy-based Active Learning becomes robust by querying more informative samples without requiring additional components. The first set of experiments studies the impact of an adapted VAT for joint-NLU tasks within low labeled data regimes. The second set shows the effect of VirAAL in an Active Learning (AL) process. Results demonstrate that VAT is robust even on multi-task training, where the adversarial noise is computed from multiple loss functions. Substantial improvements are observed with entropy-based AL with VirAAL for querying data to annotate. VirAAL is an inexpensive method in terms of AL computation with a positive impact on data sampling. Furthermore, VirAAL decreases annotations in AL up to 80% and shows improvements over existing data augmentation methods. The code is publicly available.
Statistical Equity: A Fairness Classification Objective
Venue: arXiv cs.LG 2020
Year: 2020

Machine learning systems have been shown to propagate the societal errors of the past. In light of this, a wealth of research focuses on designing solutions that are "fair." Even with this abundance of work, there is no singular definition of fairness, mainly because fairness is subjective and context dependent. We propose a new fairness definition, motivated by the principle of equity, that considers existing biases in the data and attempts to make equitable decisions that account for these previous historical biases. We formalize our definition of fairness, and motivate it with its appropriate contexts. Next, we operationalize it for equitable classification. We perform multiple automatic and human evaluations to show the effectiveness of our definition and demonstrate its utility for aspects of fairness, such as the feedback loop.
A Risk Assessment of a Pretrial Risk Assessment Tool: Tussles, Mitigation Strategies, and Inherent Limits
Venue: arXiv cs.CY 2020
Year: 2020

We perform a risk assessment of the Public Safety Assessment (PSA), a software used in San Francisco and other jurisdictions to assist judges in deciding whether defendants need to be detained before their trial. With a mixed-methods approach including stakeholder interviews and the use of theoretical frameworks, we lay out the values at play as pretrial justice is automated. After identifying value implications of delegating decision making to technology, we articulate benefits and limitations of the PSA solution, as well as suggest mitigation strategies. We then draft the Handoff Tree, a novel algorithmic approach to pretrial justice that accommodates some of the inherent limitations of risk assessment tools by design. The model pairs every prediction with an associated error rate, and hands off the decision to the judge if the uncertainty is too high. By explicitly stating error rate, the Handoff Tree aims both to limit the impact of predictive disparity between race and gender, and to prompt judges to be more critical of retention recommendations, given the high rate of false positives they often entail.
Time- and Space-Optimal Clock Synchronization in the Beeping Model
Venue: arXiv cs.DC 2020
Year: 2020

We consider the clock synchronization problem in the (discrete) beeping model: Given a network of $n$ nodes with each node having a clock value $δ(v) \in \{0,\ldots T-1\}$, the goal is to synchronize the clock values of all nodes such that they have the same value in any round. As is standard in clock synchronization, we assume \emph{arbitrary activations} for all nodes, i.e., the nodes start their protocol at an arbitrary round (not limited to $\{0,\ldots,T-1\}$). We give an asymptotically optimal algorithm that runs in $4D + \Bigl\lfloor \frac{D}{\lfloor T/4 \rfloor} \Bigr \rfloor \cdot (T \mod 4) = O(D)$ rounds, where $D$ is the diameter of the network. Once all nodes are in sync, they beep at the same round every $T$ rounds. The algorithm drastically improves on the $O(T D)$-bound of [ACGL'13] (where $T$ is required to be at least $4n$, so the bound is no better than $O(nD)$). Our algorithm is very simple as nodes only have to maintain $3$ bits in addition to the $\lceil \log T \rceil$ bits needed to maintain the clock. Furthermore we investigate the complexity of \emph{self-stabilizing} solutions for the clock synchronization problem: We first show lower bounds of $Ω(\max\{T,n\})$ rounds on the runtime and $Ω(\log(\max\{T,n\}))$ bits of memory required for any such protocol. Afterwards we present a protocol that runs in $O(\max\{T,n\})$ rounds using at most $O(\log(\max\{T,n\}))$ bits at each node, which is asymptotically optimal with regards to both, runtime and memory requirements.
Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning
Venue: arXiv cs.AI 2020
Year: 2020

Planning and reinforcement learning are two key approaches to sequential decision making. Multi-step approximate real-time dynamic programming, a recently successful algorithm class of which AlphaZero [Silver et al., 2018] is an example, combines both by nesting planning within a learning loop. However, the combination of planning and learning introduces a new question: how should we balance time spend on planning, learning and acting? The importance of this trade-off has not been explicitly studied before. We show that it is actually of key importance, with computational results indicating that we should neither plan too long nor too short. Conceptually, we identify a new spectrum of planning-learning algorithms which ranges from exhaustive search (long planning) to model-free RL (no planning), with optimal performance achieved midway.